Sub-symbol parallel inteference cancellation

ABSTRACT

Reduction of multiple access interference, in one example for asynchronous CDMA systems using long codes. In one aspect, parallel interference cancellation (PIC) implements a decoupled estimate, preferably non-linear and applied at chip intervals. According to another aspect, interference is cancelled using a technique that estimates bits for a symbol by interpolating signature waveforms for users to a common sampling lattice of the received data. According to another aspect, multi-stage, hybrid multi-stage, and reconfigurable recursive multi-stage multi-user detection architectures and corresponding processes are provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. 119(e) ofprovisional application 60/443,655, filed Jan. 30, 2003, entitled“Multi-User Detection Techniques for CDMA,” the entire contents of whichare hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to communications, more particularly toimproving communication system performance through interferencecancellation, and still more particularly to improved cancellation ofmultiple access interference in a code division multiple accesscommunications environment.

2. Description of the Related Art

Code Division Multiple Access (“CDMA”) provides an effectivecommunications technique for several users to share a communicationschannel. Unfortunately, when the channel becomes overcrowded, theconventional CDMA receiver performs poorly and multiple accessinterference (“MAI”) can severely degrade performance. Although theoptimal maximum likelihood receiver in this case is easy to describe, itis nearly impossible to implement.

Various conventional techniques examine interference cancellation at thesymbol level. Symbol-level matched filters can provide a sufficientstatistic for multi-user detection (“MUD”) in an additive white Gaussiannoise channel. This well known result concludes that the optimal userbit estimation procedure can be written at the symbol level.Accordingly, these various conventional MUD approaches use symbol-levelestimation and cancellation approaches. However, these symbol-leveltechniques are only approximations to the optimal estimator, and thereis no guarantee that these symbol level approximations fully exploit thesignal structure.

Additionally, conventional procedures can involve the followingcomputationally expensive process for canceling interference: (1)interpolating the data for each source (base station) to the samplinglattice of the signature waveform (chip center), (2) computing the bitestimates for each user, (3) synthesizing the entire symbol's binarywaveform and (4) interpolating the waveform of the whole symbol back tothe sampling grid of the data to perform the cancellation.

Some sample-level approaches have been proposed. One example uses acontinuous time (i.e., analog) maximum likelihood estimator (“MLE”)approach, which can be used as continuous decision feedback. This MLEapproach can be purposed as a single-stage analog process using filterscontrolled by relative user power levels. Although relatively easy toimplement, these approaches are not a good theoretical match to theinterference cancellation problem. To remedy such shortcomings, linearminimum mean squared error (MMSE) techniques, such as those based onstandard applications of the Kalman filter and other least-squaresgeneralizations, could be used to reduce un-cancelled interference.These techniques fully couple the users (resulting in large matrixcomputations) and perform interference cancellations in the innovationterm in the filter. Accordingly, they remain quite computationallyexpensive.

The above described techniques are also considered to be single stagealgorithms. Multiple stage designs have also been considered. Forexample, in parallel with the development of symbol-level MMSEreceivers, multi-stage parallel interference-cancellation (PIC) methodshave been developed. In multi-stage PIC formulations, code matchedfilters are applied to the difference between the receive signal and thesum of the interference signals estimated from the previous stage. Thesemultiple stage designs remain inadequate.

Each of the conventional techniques have been found to either be toocomplicated to embody in practical applications, or inadequate in termsof actual MAI cancellation in actual usage. Thus, techniques forcanceling MAI that can be practically implemented while still providingeffective cancellation remain needed.

SUMMARY OF THE INVENTION

The present invention reduces MAI in communications systems, in oneembodiment asynchronous CDMA systems using long codes.

One technique uses parallel interference cancellation (PIC) on achip-by-chip basis. Particularly, a decoupled binary minimum meansquared error (MMSE) estimate is applied for each user at each timesample, instead of waiting for a complete symbol estimate. According toanother aspect, the pseudorandom properties of the spreading codes leadto a conditional expectation based on an underlying mixture-of-Gaussians(MG) distribution. This results in performance nearly as high as thesingle-user bound, even at high loads. Furthermore, these techniquessignificantly outperform conventional ones at an affordablecomputational cost.

Another aspect of the present invention cancels multiple userinterference in a communications system wherein a plurality of userscommunicate over a shared channel by receiving a set of data (e.g.,baseband data) that provides a plurality of discrete values produced ata sub-symbol interval that is less than a full symbol period, andestimating bits for a symbol corresponding to a given user byinterpolating the signature waveforms for at least some of the pluralityof users to a common sampling lattice of the received set of data. Thisaspect can be applied to various MUD approaches including the MixedGaussian Demodulator, PIC, partial PIC, and the Decoupled KalmanDemodulator and provides a substantial reduction in complexity since theinterpolation of the binary signature waveforms can be performed easilywith lookup tables, whereas the interpolation of each source to chipcenter requires filtering operations involving traditionalmultiply-accumulate structures.

Other aspects of the present invention include hybrid multi-stagemulti-user detection (MUD) methods and a reconfigurable RecursiveMulti-Stage MUD (RMSM) algorithm architecture that, through theselection of an update gain factor and a non-linear function, canimplement various MUD algorithms. MUD algorithms supported by the RMSMarchitecture include the Mixed Gaussian Demodulator, PIC, Partial PIC,Decoupled Kalman Demodulator, and hybrid multi-stage MUD methods.

The present invention can be embodied in various forms, includingcomputer implemented methods, computer program products, communicationssystems and networks, receivers, transmitters and transceivers, and thelike.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other more detailed and specific features of the presentinvention are more fully disclosed in the following specification,reference being had to the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating an embodiment of a receiver.

FIG. 2 is a schematic diagram illustrating an embodiment of a parallelpilot channel acquisition system.

FIG. 3 is a schematic diagram illustrating an embodiment of complexambiguity function generation usable with the parallel pilot acquisitionsystem of FIG. 2.

FIG. 4 is a schematic diagram illustrating an embodiment of a activeuser detection module usable in the CDMA communications receiver of FIG.1.

FIG. 5 is a schematic diagram illustrating an embodiment of apropagation channel estimate and code tracking module usable in the CDMAcommunications receiver of FIG. 1.

FIG. 6 is a schematic diagram illustrating an embodiment of pilotgeneration usable with channel estimate and code tracking of FIG. 5.

FIG. 7 is a schematic diagram illustrating an embodiment of a pilotcancellation module.

FIG. 8 is a schematic diagram illustrating an embodiment of multistagemulti-user detection in accordance with the present invention.

FIG. 9A is a schematic diagram illustrating an embodiment of amulti-user detection processing module in accordance with the presentinvention.

FIGS. 9B-9F are schematic diagrams illustrating other embodiments of amulti-user detection processing module.

FIG. 9G is a schematic diagram illustrating another embodiment of amulti-user detection processing module, with recursive multi-stagefunctionality.

FIG. 10 is a schematic diagram illustrating an embodiment of a useramplitude estimator for a multi-user detection processing module.

FIG. 11 is a schematic diagram illustrating an embodiment of a signaturewaveform synthesizer.

FIG. 12 is a schematic diagram illustrating an embodiment of a sub-chipinterpolation filter used in the signature waveform synthesizer.

FIG. 13 is a schematic diagram illustrating an embodiment of multiplestage decoupled MUD processing.

FIG. 14 is a schematic diagram illustrating an embodiment of a stage ofdecoupled MUD processing.

FIG. 15 is a schematic diagram illustrating an embodiment of a decoupledMUD processing element.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for purposes of explanation, numerousdetails are set forth, including particular equations, in order toprovide an understanding of one or more embodiments of the presentinvention. However, it is and will be apparent to one skilled in the artthat certain specific details are not required in order to practice thepresent invention. For example, the details of one aspect of theinvention may not be required to practice another aspect of the presentinvention. For ease of description, the description is separated intoseparate sections pertaining to various aspects of the presentinvention.

As indicated, each aspect of the present invention can be embodied invarious forms, including computer implemented methods, computer programproducts, communications systems and networks, receivers, transmittersand transceivers, and the like. For example, in one embodiment a handheld device such as a cellular telephone includes conventional memory,as well as a processing unit for executing instructions provided inmemory. Conventional programming techniques are used to implement thevarious techniques described in detail in the following sections, asprovided by software that can be stored in the memory. Alternatively,the same software can be stored on various computer readable media(e.g., disks, CDs, etc.). Still further, when the instructions providedby the software are executed, computer implemented processes result.

According to one aspect, the present invention provides multi-userdetection (MUD) techniques that may be used in a CDMA communicationssystem. The MUD techniques receive complex baseband discrete time input,implement parallel interference cancellation (PIC), and performestimations at a sub-symbol level, preferably on a chip-by-chip basis.In a receiver (e.g., CDMA, cell phone), these techniques improveperformance by minimizing the potential for multiple accessinterference, and do so at relatively low computational cost. Accordingto additional aspects, the MUD techniques implement recursive multistagebased estimation and non-linear functions to further improveinterference cancellation when compared with linear and single stagetechniques.

In one embodiment, the present invention implements with the userscoupled only through the interference cancellation, which occurs on adiscrete sub-symbol sampling lattice. By way of introduction, FIGS.13-15 describe a DS-CDMA implementation using the received signal model${{y(t)} = {{\sum\limits_{p = 1}^{P}{y_{p}(t)}} + {\sum\limits_{k = 1}^{K}{{h_{k}(t)}{c_{k}(t)}}} + {v(t)}}},$with y(t)=the complex received baseband signal, h_(k)(t)=the complexasynchronous spreading functions (which can also be referred to assignature waveforms), c_(k)(t)=the complex transmitted constellationsymbol associated with the K users, and v(t)=the complex additive whiteGaussian noise. This formulation allows, if necessary, for the presenceof signals y_(p)(t) which contain known signals such as pilots,preambles, midambles, and so on. These y_(p)(t) allow for theacquisition of coherent channel information, timing, and so on as isstandard in the art. The discrete sampling interval, the time between tand t+1, is less than a symbol period and generally less than or equalto a chip period.

FIGS. 13-15 are schematic diagrams that respectively illustrate multiplestage decoupled MUD processing 1300, a single stage of MUD processing1400 in more detail, and MUD processing element 1500 in still moredetail. The schematic diagrams illustrate both the flow of suchprocessing as well as an embodiment of modular architecture for thesame.

FIG. 13 illustrates an embodiment of multiple stage decoupled MUDprocessing 1300, particularly showing how pilot interference iscancelled and then applied in a multistage setting (otherimplementations can use one stage). The multiple stages may apply thesame decoupled MUD algorithm, or, in a hybrid setting, may use differentMUD algorithms for the different stages. In one implementation, which ismost useful when only limited computational resources are available, afirst stage of MG-MUD is followed by a second stage of conventional PIC,which is itself efficiently implemented using the architecture in FIG.15. In FIG. 13, first pilot, preamble, and midamble information isprocessed 1302, if present. Information such as timing and channelequalization is shared with other blocks as needed, since in manysettings multiple users will share pilots. The pilot/preamble/midamblesignals are also reconstructed and used to cancel 1304 theircontribution to multi-access interference, resulting in y_(cp)(t), thebaseband signal after cancellation of pilots. This signal is provided tothe first stage of decoupled MUD 1306, which estimates ĉ_(k)(t) andother user state information as needed to provide transformation betweenstages. This process is described in more detail in FIG. 14. With a onesymbol delay 1312, the 1^(st) stage symbol estimates (and supportingdata) are used to seed the 2^(nd) stage MUD 1308, and so on. The finalstage MUD 1310 provides the soft decision outputs.

Here, the pilot information is estimated and the pilot signal iscancelled before user multi-access interference is estimated andremoved. This is suggested when the pilots are strong enough to estimatethe needed information. In some cases, the pilot information should bere-estimated and pilot signals re-cancelled after the intermediatestages of interference cancellation. This is advantageous, for example,when near-far problems cause weak pilots to be obscured by strong pilotsand user signals.

FIG. 14 illustrates an embodiment of a stage of MUD processing 1400.Based on estimates ĉ_(k)(t) of the constellation symbol, theinterference cancellation is achieved by subtracting 1402 the currentinterference estimate from the pilot-less baseband signal to form i(t),the innovation signal. This innovation signal represents the originalsignal y(t) with all known multi-access interference removed. Theseparate MUD processing units are coupled only through this interferencecancellation; inside of MUD processing units, the contribution of theuncancelled interference from other users is viewed as additive noise.Scalar equations for each MUD processing unit then result, in contrastto the standard Kalman filter approach which results in large matrixequations.

The interference cancellation occurs on the discrete sub-symbol samplinglattice, instead of using interpolation to move these measurements tochip center for each user or using symbol-level sampling. The decoupledprocessing units 1404 a-c use i(t) and any pilot/preamble or midambleinformation to produce an estimate ĉ_(k)(t+1)h_(k)(t+1) for this user'scontribution to MAI at the next sample time.

FIG. 15 illustrates an embodiment of a decoupled MUD processing element1500. Again, the coupling of separate users' processing units occursthrough the innovation i(t), and the signal reconstructionĉ_(k)(t+1)h_(k)(t+1) occurs at the discrete sub-symbol timescale whichis common for each user's processing unit. The signature waveformsynthesis module 1502 uses equalization and timing information, ifavailable, from embedded pilots, preambles, midambles, and so on.Through application of a one time step delay 1504, the decoupled MUDprocessor 1506 and signal reconstruction 1510 share a single calculationof h_(k)(t+1). The decoupled MUD Processor 1506 uses its internal stateinformation and the new measurement${y_{k}(t)} = {{y(t)} - {\sum\limits_{p = 1}^{P}{{\hat{y}}_{p}(t)}} - {\sum\limits_{{l = 1},{l \neq k}}^{K}{{h_{k}(t)}{{\hat{c}}_{k}(t)}}} + {v(t)}}$to make an estimate of the constellation symbol ĉ_(k)(t+1). The addition1508 of the estimated multi-access interference ĉ_(k)(t)h_(k)(t)restores the contribution of user k and simplifies the algorithm flow toproduce y_(k)(t) in the decoupled MUD processing. Although oneembodiment is described, other functionally equivalent designs can beused for FIGS. 14-15.

Another aspect of this invention is that the residual term${\sum\limits_{p = 1}^{P}\left( {{y_{p}(t)} - {{\hat{y}}_{p}(t)}} \right)} + {\sum\limits_{{l = 1},{l \neq k}}^{K}{{h_{k}(t)}\left( {{c_{k}(t)} - {{\hat{c}}_{k}(t)}} \right)}} + {v(t)}$is viewed as additive noise during signal processing, which leads tosubstantial savings in computational complexity when compared tostandard Kalman filtering and other fully coupled techniques. Theinternal states of the decoupled processor maintain the informationneeded to generate an estimate of the constellation point ĉ_(k)(t) ateach sub-symbol time step t. The decoupled MUD processor block producesan estimate at each t, instead of waiting until the end of a symbolperiod. This significantly improves cancellation at each pass (as in theMixed Gaussian MUD embodiment discussed below) and improvescomputational efficiency by allowing reuse of signature waveforms forboth demodulation and reconstruction even when applying more traditionalalgorithms (such as classic parallel interference cancellation) in thedecoupled MUD processor. In the signature waveform synthesis module1502, the signature waveform is interpolated to the sub-symbol samplinglattice of the data, rather than interpolating the data y_(k)(t) to auser k-based sampling grid, such as chip center. This produces asubstantial reduction in complexity in many cases, since the h_(k)(t+1)interpolation can often be implemented with binary lookup tables, incontrast to fixed point filters for interpolating y_(k)(t) to adifferent chip center grid for each user.

In one embodiment, these aspects can be implemented through what isreferred to as a Mixed Gaussian (MG) multi-user demodulator (referred toas MG-MUD), which implements a non-linear minimum mean square errorestimation technique, full decoupling, and multiple stages to estimateand cancel interference on a sub-symbol basis, preferably on achip-by-chip basis. Other embodiments include the Decoupled KalmanDemodulator and the Decoupled Kalman Demodulator with nonlinearrefinement, which are described further in provisional application60/443,655, filed Jan. 30, 2003, entitled “Multi-User DetectionTechniques for CDMA.” The architecture in FIG. 15 also provides anadvantageous implementation for other prior MUD techniques which updatethe symbol estimate only on the symbol boundary.

Although applicable to any communication methodology, MG-MUD isdescribed in connection with a CDMA system for ease of discussion. Thetechnique uses decoupled filters to estimate symbols for each user whileaccomplishing parallel interference cancellation on a sub-symbol basis.A minimum mean squared error estimate is made at each time sample, andinterference cancellation is performed without waiting for the completesymbol. Decoupling is accomplished through the pseudorandom propertiesof the spreading codes, resulting in an algorithm with excellentperformance even in the presence of high levels of multi-accessinterference.

By way of introduction, the MG-MUD technique is first described,followed by particular embodiments implementing the technique.

By way of example, a model using the IS95 standard with a Binary PhaseShift Keyed (BPSK) CDMA signal for K asynchronous traffic channels usinglong codes is described. Consider the received signal${{y(t)} = {{\sum\limits_{l = 1}^{K}{{h_{l}(t)}A_{l}{b_{l}(t)}}} + {v(t)}}},$with y(t)=the complex received signal, h_(l)(t) the complex asynchronousspreading functions, A_(l)(t)=the real traffic channel magnitude,b_(l)(t)=the transmitted bit, and v(t)=the complex additive whiteGaussian noise. Note, in this formulation, that the spreading functioncontains the channel effects, while the traffic channel magnitude isseparated to simplify traffic channel power tracking (relative to thepilot) in the IS95 embodiment described below. In the presence ofresolvable multipath, a formulation similar to the rake receiver isemployed. In this case, each arrival is tracked separately during MUD,with the separate measurements of a user's arrivals coherently combinedwhen making the MMSE estimate.

Here, the phase of the channel coefficient in the spreading functionsand the amplitude in the channel magnitude are estimated using standardtechniques, and the channel coefficient is assumed to be approximatelyconstant over a single symbol period.

For user k, consider a MMSE estimate {circumflex over (b)}_(k)(t) ofb_(k)(t) withπ_(k) ²(t)=E(|{circumflex over (b)} _(k)(t)−b _(k)(t)|²).

The demodulator uses a predictor-corrector structure similar to a Kalmanfilter that implements interference cancellation through the innovationsignal. Consider h_(k)(t) and A_(k)(t) to be known, and let {circumflexover (b)}_(k)(t)⁻ be the prediction of b_(k)(t) based on {circumflexover (b)}_(k)(t−1). Then $\begin{matrix}{{{\hat{b}}_{k}(t)}^{-} = \left\{ {{\begin{matrix}{{\hat{b}}_{k}\left( {t - 1} \right)} & \begin{matrix}{{if}\quad{no}\quad{symbol}\quad{transition}\quad{occurs}} \\{{for}\quad{user}\quad k\quad{in}\quad\left( {{t - 1},t} \right\rbrack}\end{matrix} \\0 & {otherwise}\end{matrix}{and}{\sigma_{k}^{2}(t)}^{-}} = \left\{ \begin{matrix}{\sigma_{k}^{2}\left( {t - 1} \right)} & \begin{matrix}{{if}\quad{no}\quad{symbol}\quad{transition}} \\{{occurs}\quad{for}\quad{user}\quad k\quad{in}\quad{\left( {{t - 1},t} \right\rbrack.}}\end{matrix} \\A_{k}^{2} & {otherwise}\end{matrix} \right.} \right.} & (1)\end{matrix}$

The demodulator is developed for a fixed user k. For notationalconvenience, assume that the user k starts a new symbol in the samplinginterval immediately before t=0. First, cancel the estimatedmulti-access interference, defining: $\begin{matrix}{{{i(t)} = {{y(t)} - {\sum\limits_{l = 1}^{K}\quad{{h_{l}(t)}A_{l}{{\hat{b}}_{l}(t)}^{-}}}}}{and}} & (2) \\{{{{i_{k}(t)} = {{i(t)} + {{h_{k}(t)}A_{k}{{\hat{b}}_{k}(t)}^{-}}}},{so}}{{i_{k}(t)} = {{{h_{k}(t)}A_{k}{b_{k}(t)}} + {\sum\limits_{l \neq k}^{\quad}{{h_{l}(t)}{A_{l}\left( {{b_{l}(t)} - {{\hat{b}}_{l}(t)}^{-}} \right)}}} + {{v(t)}.}}}} & \quad\end{matrix}$

Consider sampling at the chip rate and make an MMSE estimate of b_(k)(t)based on the vector of measurements:ĩ _(k)(τ)=Re {h _(k)(τ)*i _(k)(τ)}with τ=1, 2, . . . , t and 0≦t<the spreading gain. Note that theestimate for b_(k)(t) depends on all measurements of the current symbolup to time t. The estimate at the end of the symbol is the convergedestimate. For the BPSK case, the imaginary component ofh_(k)(τ)*i_(k)(τ) also contains limited information that does notnecessarily need to be exploited. Next used are the pseudorandomproperties of the spreading codes sampled once per chip. Then, for userk, the other users' spreading functions are considered to be randomvariables, and h_(l)(t) is approximated as independent and identicallydistributed withE(h _(l)(t))=0,E(h _(l)(t)*h _(l)(t))=H ²,E(h _(k)(t)*h _(l)(s))=0 for k≠l,andE(h _(l)(t)*h _(l)(s))=0 for t≠s.

The relative power of the users is captured in the real magnitude A_(l).Liberal application of the central limit theorem results inconditionally Gaussian distributions${{\overset{\sim}{i}}_{k}(\tau)}_{❘{b_{k}{(t)}}} \approx {N\left( {{{h_{k}(\tau)}*{h_{k}(\tau)}A_{k}{b_{k}(t)}},{{1/2}{h_{k}(\tau)}*{h_{k}(\tau)}\left( {{H^{2}{\sum\limits_{l \neq k}{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + \sigma_{\upsilon}^{2}} \right)}} \right)}$

From the pseudorandom properties of the spreading functions, it isexpected that ĩ_(k)(τ₁)_(|b) _(k) _((t)) and ĩ_(k)(τ₂)_(|b) _(k) _((t))will be approximately uncorrelated for τ₁≠τ₂. The joint density ofĩ_(k)(τ_(l))_(|b) _(k) _((t)), τ=1, 2, . . . t, is then a productdensity and the density of ĩ_(k)(τ) is a mixture of two Gaussians. Bystraightforward calculation, the minimum mean squared error estimate isthen the conditional expectation and $\begin{matrix}{{{\hat{b}}_{k}(t)} = {\tanh\left( {\sum\limits_{\tau = 0}^{t}\frac{{{Re}\left( {{h_{k}(\tau)}*{i_{k}(\tau)}} \right)}A_{k}}{{1/2}\left( {{H^{2}{\sum\limits_{l \neq k}{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + \sigma_{\upsilon}^{2}} \right)}} \right)}} & (3) \\{{\sigma_{k}^{2}(t)} = {G\left( {\sum\limits_{\tau = 0}^{t}\frac{{h_{k}(\tau)}*{i_{k}(\tau)}A_{k}^{2}}{{1/2}\left( {{H^{2}{\sum\limits_{l \neq k}^{\quad}{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + \sigma_{\upsilon}^{2}} \right)}} \right)}} & (4)\end{matrix}$

with special function G defined by $\begin{matrix}{{G(\Lambda)} = {{\frac{1}{2\sqrt{2\quad\pi\quad\Lambda}}{\int_{- \infty}^{+ \infty}{\left( {1 + {\tanh(w)}} \right)^{2}{\exp\left( {- \frac{\left( {w + \Lambda} \right)^{2}}{2\quad\Lambda}} \right)}}}} + {\left( {1 - {\tanh(w)}} \right)^{2}{\exp\left( {- \frac{\left( {w - \Lambda} \right)^{2}}{2\quad\Lambda}} \right)}\quad{{\mathbb{d}w}.}}}} & (5)\end{matrix}$

This section introduces an approximation that substantially reduces thecomputational load, while improving demodulator performance. To simplifythe demodulator, consider the approximation${{H^{2}{\sum\limits_{l \neq k}{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + \sigma_{\upsilon}^{2}} \approx {{H^{2}{\sum\limits_{l = 1}^{K}{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + {\sigma_{\upsilon}^{2}.}}$

This approximation is quite accurate for lower-powered users.Higher-powered users are easily demodulated and not significantlyaffected. Defining $\begin{matrix}{\sigma_{i}^{2} = {{E\quad\left( {i\quad(t)^{*}i\quad(t)} \right)} = {{H^{2}{\sum\limits_{l = 1}^{K}\quad{A_{l}^{2}{\sigma_{l}^{2}(\tau)}^{-}}}} + \sigma_{\upsilon}^{2}}}} & (6)\end{matrix}$allows estimation of the denominator in equation (3) directly from thetime series. A simple low pass filter{circumflex over (σ)}_(i) ²(t)=(1−α){circumflex over (σ)}_(i)²(t−1)+αi(t)*i(t)  (7)can be used,but in a specific application the filter should be more closely matchedto the dynamics of the channel. The resulting demodulator for stage 1 ofa multi-stage approach is then simply $\begin{matrix}{{{\hat{b}}_{k\quad 1}(t)}^{-} = \left\{ \begin{matrix}{{\hat{b}}_{k\quad 1}\left( {t - 1} \right)} & \begin{matrix}{{{if}\quad{no}\quad{symbol}\quad{transition}}\quad} \\{{occurs}\quad{for}\quad{user}\quad k\quad{in}\quad\left( {{t - 1},t} \right\rbrack}\end{matrix} \\0 & {otherwise}\end{matrix} \right.} & (8) \\{{i_{1}(t)} = {{y\quad(t)} - {\sum\limits_{l = 1}^{K}\quad{{h_{l}(t)}\quad A_{l}{{\hat{b}}_{l\quad 1}(t)}^{-}}}}} & (9) \\{{{\hat{\sigma}}_{i\quad 1}^{2}(t)} = {{\left( {1 - \alpha} \right)\quad{\hat{\sigma}}_{i\quad 1}^{2}\quad\left( {t - 1} \right)} + {\alpha\quad{i_{1}(t)}^{*}{i_{1}(t)}}}} & (10) \\{{S_{k\quad 1}(t)} = {\frac{2A_{k}}{{\hat{\sigma}}_{i\quad 1}^{2}(0)} \times {\sum\limits_{\tau = 0}^{t}\quad{{Re}\quad\left( {{h_{k}(\tau)}^{*}{i_{k\quad 1}(\tau)}} \right)}}}} & (11) \\{{{\hat{b}}_{k\quad 1}(t)} = {\tanh\quad{\left( {S_{k\quad 1}(t)} \right).}}} & (12)\end{matrix}$

Equations (6-10) consider the case when no resolvable multipath ispresent. When multiple arrivals occur, the arrivals are trackedseparately and information from these arrivals is coherently combined.For a user k with P_(k) multipath arrivals, equations (6-10) then become${{\hat{b}}_{k\quad 1}(t)}^{-} = \left\{ {{\begin{matrix}{{\hat{b}}_{k\quad 1}\left( {t - 1} \right)} & \begin{matrix}{{{if}\quad{no}\quad{symbol}\quad{transition}}\quad} \\{{occurs}\quad{for}\quad{user}\quad k\quad{in}\quad\left( {{t - 1},t} \right\rbrack}\end{matrix} \\0 & {otherwise}\end{matrix}{i_{1}(t)}} = {{{y\quad(t)} - {\sum\limits_{l = 1}^{K}{\sum\limits_{p = 1}^{P_{l}}\quad{{h_{lp}(t)}\quad A_{lp}{{\hat{b}}_{l\quad p\quad 1}(t)}^{-}{{\hat{\sigma}}_{i\quad 1}^{2}(t)}}}}} = {{{\left( {1 - \alpha} \right)\quad{\hat{\sigma}}_{i\quad 1}^{2}\quad\left( {t - 1} \right)} + {\alpha\quad{i_{1}(t)}^{*}{i_{1}(t)}{S_{k\quad 1}(t)}}} = {{\sum\limits_{p = 1}^{P_{k}}{\left\lbrack {\frac{2A_{k}p}{{\hat{\sigma}}_{i\quad 1}^{2}(0)}{\sum\limits_{\tau = 0}^{t}\quad{{Re}\quad\left( {{h_{kp}(\tau)}^{*}{i_{k\quad 1}(\tau)}} \right)}}} \right\rbrack{{\hat{b}}_{k\quad 1}(t)}}} = {\tanh\quad{\left( {S_{k\quad 1}(t)} \right).}}}}}} \right.$

The case of a single arrival per traffic channel is illustrated in theembodiment below.

To provide a straightforward demonstration, the above theoreticaldevelopment provides the MGMUD approach for a BPSK system. In the BPSKcase, the bits are directly estimated. Modulations with more complicatedconstellations require a different approach. This different approach isalso used in mixed modulation cases, in which different users may havedifferent modulation constellations. Consider the received signal${{y\quad(t)} = {{\sum\limits_{l = 1}^{K}\quad{{h_{l}(t)}\quad A_{l}{c_{l}(t)}}} + {v\quad(t)}}},$with y(t)=the complex received signal, h_(l)(t)=the complex asynchronousspreading functions, A_(l)(t)=the real traffic channel magnitude,c_(l)(t)=the complex transmitted constellation symbol, and v(t)=thecomplex additive white Gaussian noise.

For user k with constellation set C, we can maximize interferencecancellation by making a mean squared error estimate of theconstellation state c_(k) for this user, in contrast to the BPSK bitestimate. For complex innovation i(t), we have the MMSE estimate fromthe approximate conditional expectation $\begin{matrix}{{{\hat{c}}_{k}(t)} = \frac{\begin{matrix}{{\sum\limits_{c_{i} \in C}\quad{c_{i}\exp}}\quad} \\\left( {\sum\limits_{\tau = 0}^{t}\quad{- \frac{{{{i\quad(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{c}}_{k}(\tau)}^{-}} - {c_{i}A_{k}{h_{k}(\tau)}}}}^{2}}{{\hat{\sigma}}_{i}^{2}}}} \right)\end{matrix}}{\begin{matrix}{{\sum\limits_{c_{i} \in C}\quad{c_{i}\exp}}\quad} \\\left( {\sum\limits_{\tau = 0}^{t}\quad{- \frac{{{{i\quad(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{c}}_{k}(\tau)}^{-}} - {c_{i}A_{k}{h_{k}(\tau)}}}}^{2}}{{\hat{\sigma}}_{i}^{2}}}} \right)\end{matrix}}} & (13)\end{matrix}$with σ_(i) _(i) ² and its estimate {circumflex over (σ)}_(i) ₁ ² definedusing the same approach as in equations (6) and (7) below. Thecontribution of this user for interference cancellation is then, just asin BPSK, ĉ_(k)(t)A_(k)h_(k)(t).

Equations (1-5) implement the BPSK demodulator, while (13) describes thedemodulator for a multiple bit constellation. Both the hyperbolictangent, special function G, and other exponential functions may beimplemented as a table lookup for numerical efficiency, in which caseequations (3) and (4) can be efficiently implemented through theaccumulation of the summations. They may also be approximated, such asin the piecewise-linear approximation demonstrated in the embodimentbelow. Multiple passes are then performed by repeatedly passing throughthe data and continuing to accumulate terms in the summations inequations (2) and (3).

Equations (8-12) describe the first pass of the algorithm, which isindicated by the subscript 1 in the parameters. Remember that fornotational convenience these equations are for a user k starting a newsymbol at time t=0 and for 0≦t<the spreading gain. The summation inequation (9) restarts at each symbol boundary. In this formulation, theestimate of σ_(i) ² used in equation (9) is fixed. Use of equations(8-12) provides another significant benefit, in that the algorithm isless model-driven and provides a more robust demodulator. The algorithmneeds no estimate for the power of the additive noise, which is oftendifficult to estimate during heavy multi-access interference. Inaddition, the algorithm is no longer heavily dependent on the accuracyof the error variance dynamics in equations (4) and (5). Numericalexperiments reveal that the additive noise approximation approach, asdescribed in equations (8-12), leads to higher-fidelity approximationsin the MMSE estimation.

Several choices are available for implementing a multiple passalgorithm. For example, we may first use the earlier pass bit estimateand summations as an initial condition. For spreading gain L and user k,define F_(k)(t) to be the time index of the first sample of the currentsymbol for user k. Then, for example, the first sample of a symbol foruser k isF _(k)(t _(start))=t_(start)

and for the remaining samples in the symbolF _(k)(t)=t _(start) for t _(start) ≦t<t _(start) +L−1.

We can then write multipass equations for pass m as $\begin{matrix}{{{\hat{b}}_{k\quad 0}(t)} = {0\quad{for}\quad{all}\quad t}} & (14) \\{{S_{k\quad 0}(t)} = {0\quad{for}\quad{all}\quad t}} & (15) \\{{{\hat{b}}_{k\quad m}(t)}^{-} = \left\{ \begin{matrix}{{\hat{b}}_{k\quad m}\left( {t - 1} \right)} & \begin{matrix}{{{if}\quad{no}\quad{symbol}\quad{transition}}\quad} \\{{occurs}\quad{for}{\quad\quad}k\quad{in}\quad\left( {{t - 1},t} \right\rbrack}\end{matrix} \\{{\hat{b}}_{{k\quad m} - 1}\left( {t + L - 1} \right)} & {otherwise}\end{matrix} \right.} & (16) \\{{i_{m}(t)} = {{y\quad(t)} - {\sum\limits_{l = 1}^{K}\quad{{h_{l}(t)}\quad A_{l}{{\hat{b}}_{l\quad m}(t)}^{-}}}}} & (17) \\{{{\hat{\sigma}}_{i\quad m}^{2}(t)} = {{\left( {1 - \alpha} \right)\quad{\hat{\sigma}}_{i\quad m}^{2}\quad\left( {t - 1} \right)} + {\alpha\quad{i_{m}(t)}^{*}{i_{m}(t)}}}} & (18) \\\begin{matrix}{{S_{k\quad m}(t)} = {{S_{{k\quad m} - 1}\left( {{F_{k}(t)} + L - 1} \right)} + {\frac{2A_{k}}{{\hat{\sigma}}_{i\quad m}^{2}\left( {F_{k}(t)} \right)} \times}}} \\{\sum\limits_{F_{k}{(t)}}^{t}\quad{{Re}\quad\left( {{h_{k}(\tau)}^{*}\left( {{i_{m}(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{b}}_{k\quad m}(\tau)}^{-}}} \right)} \right)}}\end{matrix} & (19) \\{{{\hat{b}}_{k\quad m}(t)} = {\tanh\quad\left( {S_{k\quad m}(t)} \right)}} & (20)\end{matrix}$

Equations (14-20) show how new symbols are handled at each pass. Theslightly complicated time-indexing schemes in equations (16) and (19)simply restart the bit estimate and accumulator at converged estimatesfor the earlier pass whenever a symbol boundary is reached.

The multi-pass implementation in equation (19) continuously accumulatesbetween passes. To maintain the interpretation of converged symbolestimates as log likelihoods, as preferred in decoding, we mayalternately use $\begin{matrix}\begin{matrix}{{S_{k\quad m}(t)} = {{S_{{k\quad m} - 1}\left( {{F_{k}(t)} + L - 1} \right)} + {\frac{2A_{k}}{{\hat{\sigma}}_{i\quad m}^{2}\left( {F_{k}(t)} \right)} \times}}} \\{{\sum\limits_{F_{k}{(t)}}^{t}\quad{{Re}\quad\left( {{h_{k}(\tau)}^{*}\left( {{i_{m}(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{b}}_{k\quad m}(\tau)}^{-}}} \right)} \right)}} -} \\{\frac{L - \left( {t - {F_{k}(t)} + 1} \right)}{L}{{S_{{k\quad m} - 1}\left( {{F_{k}(t)} + L - 1} \right)}.}}\end{matrix} & (21)\end{matrix}$

This function linearly removes the initial condition in the accumulator.A third approach is to save all of the matched filter values$\frac{2A_{k}}{{\hat{\sigma}}_{i\quad m}^{2}\left( {F_{k}(t)} \right)}{Re}\quad\left( {{h_{k}(\tau)}^{*}\left( {{i_{m}(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{b}}_{k\quad m}(\tau)}^{-}}} \right)} \right)$in a circular buffer, which is filled with data from the symbol as thenew data is available. The entire buffer is then summed at each timestep. In this case, $\begin{matrix}\begin{matrix}{{S_{k\quad m}(t)} = {\frac{2A_{k}}{{\hat{\sigma}}_{i\quad m}^{2}\left( {F_{k}(t)} \right)}{\sum\limits_{F_{k}{(t)}}^{t}\quad{{Re}\quad\left( {h_{k}(\tau)}^{*} \right.}}}} \\{{\left. \left( {{i_{m}(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{b}}_{k\quad m}(\tau)}^{-}}} \right) \right) + \frac{2A_{k}}{{\hat{\sigma}}_{{i\quad m} - 1}^{2}\left( {F_{k}(t)} \right)}}\quad} \\{\sum\limits_{t + 1}^{{F_{k}{(t)}} + L - 1}\quad{{Re}\quad{\left( {{h_{k}(\tau)}^{*}\left( {{i_{m - 1}(\tau)} + {{h_{k}(\tau)}\quad A_{k}{{\hat{b}}_{{k\quad m} - 1}(\tau)}^{-}}} \right)} \right).}}}\end{matrix} & (22)\end{matrix}$

In practice, equation (22) would be implemented by subtracting the oldterm and adding the new term. Each of the three techniques (19), (21)and (22) provide increased accuracy in estimating the bit loglikelihoods at the cost of increased implementation complexity.

Thus, described herein is a practical multi-user detection technique forhigh user loads. Through decoupled filters based on the underlying mixedGaussian distributions, the technique cancels interference on achip-by-chip basis instead of waiting for a complete symbol estimate.Further numerical efficiency results from estimating the un-cancelledinterference power from the time series itself, instead of using amodel-based approach. This technique compares favorably to an optimizedpartial PIC algorithm using the IS95 standard. This embodimentillustrates various features of the invention. First, the separate MUDprocessor blocks are coupled only through the interference cancellation.Second, this interference cancellation occurs on the data samplinglattice (as compared to individual user chip center or the symbol levellattice) using the sub-symbol level structure introduced in FIG. 15Finally, the interference cancellation begins at a sub-symbol level,without waiting for demodulation of a complete symbol as in the priorMUD art.

Another aspect of the present invention is provision of hybridmulti-stage (or multi-pass) MUD techniques that use differentsample-level methods at each stage as introduced in FIG. 13. The variousMUD techniques described above can, for example, be respectively used asthe differing sample-level methods. Alternatively, a hybrid solutioncould include the use of a DKD or MG-MUD first stage followed by aconventional Partial Parallel Interference Cancellation (PPIC). In oneembodiment, the hybrid solution allows each stage to consist of adifferent method (e.g. DKD, MG-MUD, PIC, PPIC). To accommodatecomputational efficiency, the current stage preferably includesfunctions that compute the ancillary method-specific parameters neededby the next stage.

FIG. 1 is a schematic diagram illustrating an embodiment of a CDMAcommunications receiver (SSCR) 100 and corresponding processes. The SCCR100 includes a decimation module 102, interpolation module 104, pilotacquisition module 106, code tracking and channel estimation (CTCE)module 108, active user detection module 110, delay buffer 112, pilotcancellation module 114, and multi-user detection (MUD) module 116

Although the present invention is applicable to various communicationssystems, for ease of description some example are described in thecontext of usage with the IS95B CDMA standard. The input to the SSCR 100is a digitized complex baseband signal where the sampling rate of thesignal can be at any integer multiple (usually 1, 2 or 4) times thechipping rate, which in the case of IS95 is 1.2288 million chips persecond. For the system described, a version of the signal digitized at 1sample of chip is needed as is a version sampled at a rate of at least 4samples per chip. If the input is clocked at 4 samples per chip, thenthe decimation module 102 uses conventional decimation techniques toobtain a version clocked at 1 sample per chip. If the input is clockedat 2 samples per chip, then the interpolation module 104 usesconventional interpolation techniques to generate a version sampled at 4samples per chip as is used by the active user detection module 110,with decimation being used to generate a version sampled at 1 sample perchip for use by the rest of the system. Finally, if the input is sampleda 1 sample per chip as in the figure, then interpolation is used togenerate a version at 4 samples per chip.

With reference to the pilot acquisition module 106, each CDMA basestation (called a source) emits a pilot signal that is used foracquisition of code timing. In IS95B, the pilot signal uses a repeating32768 chip code sequence. Each base station has a different timingoffset from its neighbors. In the pilot acquisition module 106, thenumber of sources, and their timing offsets, and optionally Doppleroffsets are estimated. In the exemplar system, timing offsets accurateto 1/16 of a chip are used. Additionally, a preliminary estimate is madeof the complex amplitude of the channel. The result provided by thepilot acquisition module 106 is a list of sources, along with theirtiming offset, Doppler offset, and complex amplitude.

Preferably, the active user detection module 110 uses a complex basebandinput signal of at least 4 samples per chip. If the input to the systemis less than 4 samples per chip, interpolation is performed.Additionally, the list of sources and their respective parametersderived by the pilot acquisition module 106 are used. Furthermore, theremay be a list of known or required users. In IS95, such a list wouldnormally include paging and synch channels and the receiver user's ownchannel. The active user detection module 110 attempts to identify whichof the available sub channels (A CDMA base station has 64 sub-channels,including pilot, paging, synch, and traffic channels) have users on itby comparing the power seen in that channel to a threshold. The outputof the active user detection module 110 is a list of users for eachsource, along with their corresponding channel index and amplitude.

The CTCE module 108 takes in the complex baseband input signal sampledat one sample per chip and correlates it with a pilot signal at −½, 0and ½ chip delays. The correlation with the pilot at 0 delay is used toestimate the channel's complex amplitude, while the correlations atdelays of −½ and ½ are used to track changes in the timing offset. Theoutput of the CTCE module 108 is a list of sources, their updated timingoffset, Doppler offset, and complex channel amplitude.

The pilot cancellation module 114 takes the complex baseband signalsampled at 1 sample per chip as its data input and the list of sourcesand their timing offsets, Doppler offsets and complex channelamplitudes. It then uses the source information to synthesize a replicaof the pilot for each source which it then subtracts from the complexbaseband input. The output of the pilot cancellation module 114 is apilot-less complex baseband signal which is fed into the MUD module 116.The MUD module 116 also uses the list of sources and their correspondingtiming offsets, Doppler offsets and complex channel amplitudes, and thelist of users and their corresponding Walsh code index, and amplitude.

The MUD module 116, in conjunction with remaining components performsinterference cancellation by receiving and processing a discretelysampled waveform, performing estimation at a sub-symbol level,preferably down to the chip level, and incorporating parallelinterference cancellation. Non-linear estimation and multistagearchitecture may also be provided, as described further below.Preferably, the MUD module 116 applies the previously described MG-MUDfunctionality. A more detailed embodiment of the MUD module 116including components for carrying out such functionality is describedfurther below.

The output of the MUD module 116 is a stream of soft decision symbolsthat are fed to the back end for error correction decoding andsubsequently either the output data stream or into a vocoder to produceaudio output.

The SCCR 100 internals may be provided as software, hardware, firmware,or any possible combination of hardware, firmware and/or software. TheSCCR 100 may also be variously implemented such as on anApplication-Specific Integrated Circuit or on a Digital SignalProcessor, which include elements for executing the software or thelike. The preferred implementation solution will depend on ease ofintegration with the overall system design.

FIG. 2 is a schematic diagram illustrating an embodiment of pilotacquisition 200 and corresponding modular architecture in accordancewith the present invention. The figure describes an embodiment in whichsignificant Doppler occurs and is compensated for. Depending on mobilespeeds and frequency band, smaller Doppler effects might instead becompensated for by code tracking alone. The input to the system 200 is afixed-length sequence of complex baseband samples, sampled at the chiprate. There is a tradeoff in the number of input samples used in thepilot acquisition. Increasing the samples improves the signal-to-noiseratio (SNR) of the channel estimates for each source, but it alsoincreases the Doppler resolution, which means that far more computationmust be performed to correctly estimate the Doppler offset. In theexemplar system, 8192 input samples are used in the pilot acquisition.The first component in pilot acquisition 200 is generation 202 of thecomplex ambiguity function. Let M_(da) be the length of the input datasequence used for Pilot Acquisition, and let N be the number ofpositions in the code (32768) in the case of IS95. The CAF is thecorrelation between the input sequence and a periodic replication of thepilot signal that is provided for CAF generation 202 (“pilot signalreplica”). The correlation is computed between the input sequence andthe complex conjugate of the pilot signal with the appropriate code andDoppler offset.

For each Doppler offset, the correlation at N positions is calculated.For each point in the CAF, the magnitude squared is computed 202. Aremoval-of-outliers approach is used with a noise threshold 204 a togenerate noise statistics 204 b. From this, a threshold is computed 204c and the CAF magnitude squared is compared to this threshold 204 d.Positions whose corresponding magnitude squared exceeds a threshold areidentified and added to a list of “mountains” 204 d. Points on this listof mountains are clustered to identify CAF points corresponding to thesame source. Maintained along with each mountain are the timing offset,the Doppler offset, and complex amplitude of each point 206.Additionally the same information is also maintained for the twoadjacent Doppler bins for each point.

Timing offsets are then refined with a successive approximationprocedure 208. For each cluster, the point with the largest magnitudesquared is selected, and the point corresponding to one of the twoadjacent Doppler bins with the larger magnitude of the two is alsoselected. The Doppler offset is computed by interpolating the Doppleroffsets of the two points. The interpolation assumes that the CAFsurface will have a sin x/x shape about the peak. Once the Dopplerinterpolation is completed, a pilot signal is synthesized and correlatedwith the same timing offset at the interpolated Doppler peak. The inputsignal is then correlated with the synthesized pilot and the complexamplitude is computed. The correlations are also computed with a timingoffset of −½ and ½ chip from this point. A successive approximationprocedure is used to refine the Doppler offset estimate to the requiredresolution. In the exemplar, this resolution is 1/16th of a chip. Foreach of the iterations, in successive approximation, three points (twointervals) are necessary. Starting with the two intervals alreadyidentified [−½, 0], and [0, ½], the interval whose magnitudes sum to alarger value is selected, and, for example the point at offset ¼ chip iscomputed. The iteration continues until we have a point at resolution1/16th of a chip.

FIG. 3 is a schematic diagram illustrating an embodiment of computing300 the CAF using the fast Fourier transform (FFT). In particular for agiven Doppler offset, the set of needed correlations can be obtained byperforming a circular convolution of the input sequence with the pilotsequence. One relatively fast method of performing circular convolutionis to take the discrete Fourier transform 302, 312 of both signals,point-wise multiply 308 the results together, and compute 316 theinverse discrete Fourier transform. The FFT is a fast algorithm forcomputing the DFT. The pilot signal replica may also be filtered 304prior to application of the discrete Fourier transform 312. Theresultant pilot signature waveform can be stored in the pilot buffer314. In the case of IS95 since the pilot signal is 32768 samples long,the input signal is zero-padded 302 to form fill a buffer of size 32768.Then the FFT of the input buffer is computed. For the case of zeroDoppler offset, FFT of the input buffer is point-wise multiplied 308with the pre-stored pilot FFT. The result is passed through an inverseFFT 316 to produce the CAF values for all integer timing offsets at zeroDoppler and retained 318 in the CAF buffer. For other Doppler shifts,the pilot signal is circularly shifted 310. Each circular shift N is onefrequency slice of the CAF, with the collective slices comprising thefull CAF. The threshold is chosen to achieve a tradeoff betweendetecting a remote pilot and producing false alarms.

FIG. 4 is a schematic diagram illustrating an embodiment of a userdetection module 400 including multiple user detection sub-modules 400a-c. The input to the user detection module 400 is a complex basebandsignal having sampling rate of at least 4 times the chip rate. In theexemplar, a sampling rate of 4 times the chip rate is used. Also inputto the user detection module 400 is the list of sources, their timingoffsets, Doppler offsets, and complex amplitudes. The search for usersoperates independently on each source. For each source, the phase of theinput that is most closely aligned with the chip center is chosen, andthe input is decimated 402 by a factor of four. The resulting signal isthus closely aligned with the pilot sequence. The decimated signal isthen complex multiplied by the complex conjugate of the complex channelamplitude and then real and imaginary parts are multiplied by theircorresponding pilot sequences and the results are summed together. Thenthe number of possible users is correlated 404 across the relevantnumber of chips. Preferably, when sixty-four samples aligned with asymbol are complete, a Hadamard transform is calculated which performs acrude demodulation on all sixty-four Walsh channels. Following thisstage, the power for each channel is accumulated 406 over a specifiedtime interval, for example five-hundred symbol periods. A threshold iscomputed 408 based on noise statistics using a noise threshold todetermine the noise samples. The noise threshold is chosen to balancethe competing interests of increased interference cancellation, limitedcomputational capacity, and the cost of false alarms at the expecteddesign point. For each channel, if the power is determined 410 to exceeda threshold, the user is determined to be active and its amplitude isestimated as the ratio of its power to the pilot power.

FIG. 5 is a schematic diagram illustrating code tracking and channelestimation 500 performed by the CTCE module and corresponding modulararchitecture. Again, the input is the complex baseband input signalsampled at one sample per chip, along with the list of sources, theirtiming offsets, Doppler offsets and complex channel amplitudes. Each ofseveral parallel CTCE blocks 500 a-c contains correlation 502, pilotgeneration 504, code tracking 506, channel estimation 508, squaring 510,and prompt pilot energy accumulation 512 modules. Pilot generation 504is provided by the signature synthesis module 100 in FIG. 11, asdiscussed below. Preferably correlation is performed by a three-tapcorrelator, a variation of the standard early-late gate delay-lockedloop (DLL). In most DLL's, a fixed pilot is correlated with the inputsignal being delayed and advanced by a ½ chip. However, in oneembodiment of the present invention, so that the input signal need onlybe available one sample per chip, a pilot signal delayed by ½ chip iscomputed. This describes the implementation of an early-late gate DLLimplemented in the code tracking module 506. Channel estimation 508(amplitude and phase) follows from a correlation of the prompt pilot anddata in the code tracking loop. The prompt pilot is also squared inelement 510 and accumulated in 512 to calculate the prompt pilot energyfor use in the channel estimation element 508.

FIG. 6 is a schematic diagram illustrating pilot generation 600performed by the CTCE module and corresponding modular architecture. Thepilot is generated 602 and filtered 604 with no delay to produce theprompt pilot, and filtered 606 with a −½ chip delay to produce the earlypilot. The early pilot is then delayed 608 by 1 chip to obtain the pilotwith +½ chip delay, referred to as the late pilot. Each of these pilotsis correlated with a complex input signal.

After a designated period, in the exemplar every 512 chips, an errormetric is calculated as follows: (1) each of the three correlations(early, late and prompt) is multiplied by its complex conjugate tocalculate early energy, prompt energy and late energy; and (2) the errormetric is calculated as (early energy−late energy)/prompt energy.

The update to the timing offset is given by some feedback coefficienttypically 0.1-0.3, multiplied with the error metric. The estimate of thechannel's complex amplitude is calculated by dividing the promptcorrelation (before squaring) by the energy in the prompt pilot. Oncethe update to the timing offset and update to the channel's complexamplitude are calculated, the four accumulators (early, late, prompt,and pilot energy) are initialized to zero, and the processing continues.

FIG. 7 is a schematic diagram illustrating pilot cancellation 700performed by the pilot cancellation module. The input to pilotcancellation 700 is the complex baseband input signal sampled at 1sample per chip. Additionally the list of sources, their timing offsets,Doppler offsets and complex channel amplitudes are taken from theoutputs of the CTCE module. These parameters are used to generate 702a-c the pilot signal for each source. This pilot is then multiplied bythe complex channel amplitude. The pilots are summed and then subtractedfrom the complex baseband data to provide pilot-less complex basebanddata as shown. The output of the pilot cancellation module is fed intothe data input of the MUD module.

FIG. 8 is a schematic diagram illustrating an embodiment of multistagemuiti-user detection (MUD) 800, such as performed by the previouslyintroduced MUD module in accordance with the present invention.Particularly, the described case involves K users using 64 chips persymbol, with three stages used in the detection. The multistage MUD 800receives the pilot-less complex baseband input at 1 sample per chip andproduces soft symbol estimate and bit estimate outputs.

Each MUD stage 800 a-c is built around one or more MUD ProcessingElements (MUDPE), preferably matching the number of users (K),sixty-four in the described example. For ease of depiction, three MUDPEs804 a-c are shown. A MUDPE contains two basic functions: a demodulatorthat decodes the input and estimates the current symbol, and asynthesizer, which based on the estimate of the symbol estimates thecontribution of the current user to the next chip. For a given stage,the outputs of all MUDPE's 804 a-c are summed together to form anestimate of the next chip of the pilot-less baseband input. The currentchip's estimate for the stage (which would have been computed on theprevious chip) is then subtracted from the pilot-less baseband input toform the innovation signal. This innovation is the component of thepilot-less baseband that cannot be predicted out. The innovation signalfor a given stage is the input to all MUDPE's 804 a-c for that stage 800a.

Each MUDPE 804 a-c produces two additional outputs either to initializethe next stage for a given user, or as the final soft decision outputfor the user of interest. The first output is the soft decision outputfor that stage. For each user it is the linear accumulator of a matchedfilter operating on the pilot-less baseband input with the multi-accessinterference removed. Internal to the MUDPE, this pilot-less basebandinput with the user's multi-access interference removed is formed as thesummation of the innovation with the MUDPE's prediction of the user'scontribution to the pilot-less baseband. For the first stage, thisaccumulator is initialized with zero. For later stages, this accumulatoris initialized with the soft decision output of the previous stage.

The second output of the stage is the initial bit, (or in the case ofnon-BPSK modulation the initial constellation point) estimate for thenext stage. This bit estimate is used for the initial bit estimate onthe first chip of a given symbol processed by a stage. For the firststage, the bit estimate is zero. The actual bit is either −1 or +1.However, there are at least three approaches to producing a soft bitestimate internal. The first approach is to use a hard decision limiter,which is simply the sign of the soft decision accumulator. The secondapproach, which produces the optimal MMSE estimate is to compute thehyperbolic arctangent of the soft decision accumulator. The third andpreferred approach approximates the hyperbolic arctangent function usinga piecewise linear function whereby the output is equal to the input ifthe magnitude of the input is less than 1, but is clipped to either −1or 1 if the magnitude is greater or equal to 1.

Both the soft decision output and the bit estimate outputs are latchedduring the processing for a given symbol. The latch is clocked at theend of the completed symbol. For IS95, a symbol is 64 chips. Thereforethe input to a next stage is delayed 802 a, 802 b by the number of chipsin a symbol since the output of the current stage won't be ready untilit has processed all chips for a symbol. Similarly, a buffer of the sizeof the number of chips in a symbol is preferably be placed on the inputbetween each successive stage.

FIGS. 9A and 9B are more detailed schematic diagrams of MUDPEs 900 a,900 b. The input i(t) is the complex innovation. The complex variabley_(k)(t) is the synthesis of the contribution to the pilot-less basebandfor user k. As indicated in FIG. 9A, this contribution isy_(k)(t)=h_(k)(t)A_(k){circumflex over (b)}_(km)(t)⁻ for user k, stagem. The contribution y_(k)(t) for user k and the innovation i(t) aresummed together 924 to restore the contribution from user k. This formsthe approximation i_(k)(t) of the pilot-less baseband signal for user kwith all multi-access interference removed according to the followingequation:i _(k)(t)=i(t)+h _(k)(t)A _(k) {circumflex over (b)} _(km)(t)⁻

The MUDPE 900 a includes a signature synthesizer 906 which receives thetiming offset and the Walsh index for the user, and calculates thesignature waveform. Calculation of the signature waveform is describedfurther below with reference to FIG. 11.

The user estimator 902 calculates an estimate of A_(k), the user'scomplex amplitude. The user's complex signature waveform A_(k)h_(k)(t+1)is constructed from the multiplication 936 of the user's complexamplitude estimate and the signature waveform. This waveform is computedduring the current chip to estimate the user's contribution to the nextchip, The one-chip delay provided by delay 914 d accommodates providingthe appropriate value for the contribution to the current chip.

For the receiver (which can be viewed equivalently as a matched filteror as a correlator), i_(k)(t) is multiplied by the complex conjugate ofthe signature waveform and then the real part of that product is takento provide a matched filter term. This functionality comprises (1)multiplying 926 the real part of i_(k)(t) with the real part of thesignature waveform, (2) multiplying 928 the imaginary part of i_(k)(t)with the imaginary part of the signature waveform, and (3) adding 930the two products together, yielding the real component thereof. Thisvalue is provided to accumulator 912. In conjunction with feedbackpassed through delay element 914 a, which passes the prior chipaccumulated value to the accumulator 912, which effectively accumulatesthe value for input to the user amplitude estimator 902, which is usedin user amplitude estimation as described further with reference to FIG.10(10 or 11?) below. At every symbol boundary, the accumulator iscleared by multiplexing 944 in a zero.

In order to normalize the accumulator 934, the matched filter outputvalue is scaled by 2 times the reciprocal of an estimate of theinnovation variance (2/σ²), through multiplier 932. A running estimateof the innovation variance can be calculated outside the MUDPE 900 a bycomputing the following running sum: 0.01× the current innovationsquared plus 0.99 times the previous value in the accumulator 934.

The normalized matched filter output value for the current chip isprovided to the accumulator 934 for the soft symbol output S_(km)(t).The accumulator 934 also receives the previous accumulated value throughdelay 914 c, which thereby retains an accumulated value for the softsymbol, incremented on a chip-by-chip basis. The soft symbol outputS_(km)(t) is provided to latch 908 a, which is clocked at the symbol endto store the accumulated output S_(km)(F_(k)(t)+L−1) for the user k fora full symbol period.

The soft symbol output S_(km)(t) is also passed through a bit estimatecomputing module 904. In one embodiment, the bit estimate computingmodule 904 implements a non-linear computation, more particularly apiecewise linear approximation to the hyperbolic tangent function. Inalternative embodiments, other non-linear computations, or a linearcomputation may be used for the bit estimation. The resultant bitestimate {circumflex over (b)}_(km)(t) is output to latch 908 b, whichis clocked at the symbol end to provide the final bit estimate{circumflex over (b)}_(km)(t+L−1). This latch 908 b provides the softbit estimate for this user k for this stage m at the end of the symbolperiod.

The multiplexer 942 controls the predicted a priori bit estimate{circumflex over (b)}_(km)(t−1)⁻. If (t+1) represents the first chip ina symbol, the multiplexer selects the bit estimate from the previousstage or 0. Otherwise, {circumflex over (b)}_(km)(t+1)⁻={circumflex over(b)}_(km)(t)

The predicted bit estimate {circumflex over (b)}_(km)(t+1)⁻ is alsomultiplied 938 by the previously described signature waveformA_(k)h_(k)(t+1). To allow cancellation at the next time step, thisprediction is fed forward to the accumulation of the innovation signalfor the next time step. The result is the prediction of the user'scontribution to the signal for the next chip. This quantity is both fedback through a chip delay 914 b to be summed with the next innovationsignal for the next chip (as h_(k)(t)A_(k){circumflex over(b)}_(km)(t)⁻), and also output from the MUDPE 900 a to be added to thepredictions of all of the other users.

The MUDPE 900 a also operates in conjunction with the previouslyintroduced multistage processing. To accommodate this, at the beginningof a symbol, accumulator 934 takes its input from the accumulated softsymbol from the previous stage and is selected by multiplexer 940. Ifthere is no previous stage, then a zero is input as the accumulated softsymbol value.

The MUDPE 900 a functionality may be embodied within a receiver. It maybe provided as software, or also as hardware, firmware, or any possiblecombination of hardware, firmware and/or software. The MUDPE 900 asoftware may also be part of a computer system wherein its instructionsare executed by a processor. It may also take the form of a storagemedium that stores the software, such as an optical disc in CD or otherformats, magnetic storage, flash memory, or others.

It is noted that although conceptually, there is one MUDPE 900 a foreach user for each stage, it is also possible to embody multiple logicalMUDPEs as a single physical MUDPE 900 b as indicated in FIG. 9B. Thisarrangement would be most useful in a hardware implementation.Generally, the MUDPE 900 b is similar to MUDPE 900 a and to that end thesimilarly numbered items operate as described above. However, in lieu ofindividual latches 908 a,b, the requisite number N of latches 920 a,bare used, and in lieu of chip delays 914 a-d, “N chip” delays 922 a-bare used. Additionally, the User Amplitude Estimator, and SignatureSynthesizer blocks have to be modified to have memory so that they canmultiplex their outputs for the N respective users. Functionally, theMUDPE 900 b operates like the previously described MUDPE 900 a, withover-clocking and the addition of buffers. There is also an accumulatorand clock delay at the output to add the contributions of the differentusers together. While the innovation signal input and the accumulateduser contributions at the output are still clocked at the chip rate, theMUDPE 900 b internals are clocked at N times the chip rate. The softsymbol output and the bit estimate outputs must also be synchronizedwith the next stage using symbol rate clocking.

There are several different approaches to combining estimates fromdifferent stages together. FIGS. 9 c-9 f describe four alternatives.

FIG. 9 c is a variation on the MUDPE. In order for the accumulated softdecisions to have the interpretation as “log-likelihoods” theaccumulation of matched filter output must be effectively carried outover 1 symbol period. This is achieved in FIG. 9 c by dividing 946 theaccumulated soft symbol in the previous stage by the number of chips ina symbol and subtracting 948 it from the current matched filter termusing subtraction element. At the end of a symbol period, the entireaccumulated soft symbol from the previous stage would have beensubtracted so that the accumulation would be that of the matched filterterm from the current stage.

FIG. 9D is another variation on the MUDPE. In this variation, instead ofsubtracting out an average from the previous stage, the actual matchedfilter terms are passed between stages and subtracted out. Morespecifically, the matched filter term, scaled by the innovation variance(2/σ²), for each chip is passed into a first-in-first-out (FIFO) bufferelement 950 and is clocked out at the chip rate. A signal representingthe scaled matched filter term from the previous stage is an input andis subtracted from the current scaled matched filter term usingsubtraction element 948. The net result is that at every chip, theaccumulator contains an exact accumulation using the scaled matchedfilter term for each chip. For chips from the beginning of the symbol tothe current symbol the accumulation has the newest value, and for chipsafter the current chip, the accumulation has the value used on theprevious stage. The advantage of this technique is that does not need toapproximate the value to be subtracted off by its mean value. Thedisadvantage is that it requires and additional FIFO buffer.

FIG. 9E is another variation of the MUDPE that could be used for thefirst stage of MG-MUD. This variation involves merging of the functionsof accumulator (912, FIG. 9A) into accumulator 934, and the placement ofthe multiplication element 932 at the output of the accumulator 934rather then the input. If this variation had been used on the firststage, then both accumulators 912 and 934 would have been initializedwith 0 anyway. Similarly, on the first chip, the multiplexing element942 would choose the bit estimate as 0 and on the next N−1 chips, whereN is the number of chips per symbol, choose the bit estimate from theoutput of the nonlinearity.

FIG. 9F is a variation that is similar to FIG. 9E. It is used toimplement a PIC algorithm using this architecture. The primarydifference is that elimination of multiplexing element (942, FIG. 9E)altogether. The current bit estimate {circumflex over (b)}_(km)(t), theoutput of the non-linearity 904 is latched at the symbol end. Theestimation used in the prediction, {circumflex over (b)}_(km)(t+1)⁻, istaken as the estimate from the previous stage.

According to still another aspect of the present invention, areconfigurable architecture implements various MUD methods through theselection of an update gain factor and a non-linear function. Thisarchitecture (referred to as the Recursive Multi-Stage MUD (RMSM)algorithm architecture) is a multi-stage, sample-level implementation ofthe basic functions common to various MUD methods. The common functionsinclude multi-stage state prediction and update equations and diagonalgain matrix update equations. The RMSM architecture is configured to aspecific MUD method by calculating and applying the time andstage-dependent gain factor that corresponds to that method. Theconfiguration also requires the selection of a method-specificnon-linear function used for symbol estimation and decision, and theselection of a method-specific state update equation. MUD algorithmssupported by the RMSM architecture include the Mixed GaussianDemodulator, PIC, Partial PIC, Decoupled Kalman Demodulator, and hybridmulti-stage MUD methods.

FIG. 9G illustrates an embodiment of the MUD processing element 900 gembodying the RMSM architecture. This processing element 900 gimplements the functionality of the processing elements depicted inFIGS. 9A-9E in a single architecture. The processing element 900 gcontains additional switches 952, 954, 956, accommodates theintroduction of different sets of gain factors β_(km)(t) and subtraction956 of likelihood related terms ξ_(km)(t).

FIG. 9G has been variously simplified but is otherwise consistent withFIGS. 9A-F. First, it illustrates the non-linear decision function 904generally. As with other embodiments, various non-linear decisionsfunctions may be applied, including but not limited to the tan hfunction depicted in some of the figures. Additionally, the complexnumber pathways are shown in a single bold line in lieu of two lines.Accordingly, the function of multiplier 928 is merged into multiplier926. Complex multiplier 926 multiplies the incoming signal by theconjugate of the synthesized signature waveform. Function 964 performsthe conjugation operation. Since this design embodies an architectureable to implement various other algorithms, the reciprocal of themagnitude scaling function 962, switch 952, and multiplier are providedso different gain factors b(t) can be used and so the user amplitude canbe calibrated out. Further, the functionality provided by respectivemultiplexers and delays is not shown but is understood to be merged intothe illustrated accumulators 912, 934.

By selecting the right set of gain factors, setting various switches,and selecting the desired non-linear decision function, this processingelement 900 g can easily be reconfigured to perform a single stage ofany of various MUD algorithms, such as PIC, PPIC, DKD, MGMUD, or varioushybrid multi-stage methods.

Often, the method-specific set of gain factors can be pre-computed andstored in a table. In its most general form, the size of each table is a[N×M×K] table where N is the number of chips/symbol, M is the number ofstages, and K is the total number of users (or channels). The currentuser, the current processing stage, and the current chip within a symboldetermine the indices into a table.

The gain-factor vectors β_(km)(t) are a function of the currentalgorithm in effect and the stage number.

For PIC, the gain factors are independent of both the stage and user andare:${\beta\quad\left( n_{k} \right)} = {{\frac{1}{n_{k}}{where}\quad n_{k}} = \left\{ {1,\ldots\quad,N} \right\}}$is the current chip index within the symbol and N is the number chipsper symbol.

The gain factors for the Partial PIC algorithm is similar to PIC butinclude a stage dependent weighting:${\beta_{m}\left( n_{k} \right)} = {{\frac{\lambda_{m}}{n_{k}}\quad{where}\quad 0} \leq \lambda_{m} \leq 1.}$Normally, the lm approaches 1.0 as the stage number increases.

As the name implies, the gain factor for the Block-structured Fixed-gainKalman Demodulator (BFKD) is simply${\beta_{m}\left( n_{k} \right)} = {\frac{\alpha_{m}}{N}\quad{where}\quad\alpha_{m}}$

takes on a user defined value between 0 and 1. Refer to B. Flanagan andJ. Dunyak, “Steady State Kalman Filter Technique for MultiuserDetection,” Proceedings of the IEEE Milcom 2003 Conference, Oct. 13-16,2003, for algorithm description and related references.

Gain factors for the Decoupled Kalman Demodulator (“DKD Gain Factors”)can be defined according to J. Dunyak, “A Decoupled Kalman FilterTechnique for Multiuser Detection of Pulse Amplitude Modulation CDMA,”IEEE Proc. of Wireless and Optical Communications, 2002.

It is assumed that one of several non-linear decision functions can beselected depending on the desired algorithm desired. Candidate functionsinclude the hard-limiter, the sign function, the clipping limiter,erasures, and the hyperbolic tangent. An Erasure is a 3-level functionthat assigns an output of −A, 0, +A depending on the input signal.

As stated previously, with but a change of a few parameters, the RMSMarchitecture can be adapted to a specific algorithm. Referring to FIG. 9x, the configuration for each specific algorithm are as follows:

For PIC:

-   -   1. Use the gain factors for PIC    -   2. Set switch A so the gain factor is scaled by the inverse of        the absolute value of the user amplitude    -   3. Set switch B so the regenerated signal is added to the input        complex baseband innovation i(t)    -   4. Trigger switch C so the non-linear symbol estimate from the        previous stage is used every time.    -   5. Select desired non-linear detection function with an        preceding 1/N scaling    -   6. Set the likelihood term ξ_(km)(t)=0, where N is the number of        chips/symbol.

For PPIC:

-   -   1. Use the gain factors for Partial PIC    -   2. Set switch A so the gain factor is scaled by the inverse of        the absolute value of the user amplitude    -   3. Set switch B so the regenerated signal is added to the input        complex baseband innovation i(t)    -   4. Trigger switch C so the non-linear symbol estimate from the        previous stage is used every time.    -   5. Select desired non-linear detection function with preceding        1/N scaling    -   6. Set the likelihood term ξ_(km)(t)=0

For MG-MUD:

-   -   1. Use the gain factors for MG-MUD    -   2. Set switch A so the gain factor is scaled by 1    -   3. Set switch B so the regenerated signal is added to the input        complex baseband innovation i(t)    -   4. Trigger switch C so the current non-linear symbol estimate is        used every time except at the beginning of a symbol boundary. In        which case, non-linear symbol estimate from the previous stage        is used.    -   5. Select the hyperbolic tangent or a clipping limiter    -   6. To implement FIG. 9 a version of MG-MUD, set the likelihood        term ξ_(km)(t)=0. To implement FIG. 9 c version, set ξ_(km)(t)        (soft symbol estimate from previous stage)/N. To implement FIG.        9 d version, set ξ_(km)(t) equal to the corresponding matched        filter term from the previous stage.

For DKD:

-   -   1. Use the above introduced DKD Gain Factors    -   2. Set switch A (952) to 1    -   3. Set switch B to 0    -   4. Trigger switch C so the current non-linear symbol estimate is        used every time except at the beginning of a symbol boundary. In        which case, non-linear symbol estimate from the previous stage        is used.    -   5. Select <TBD> non-linear function    -   6. Set the likelihood term ξ_(km)(t)=0

For BFKD:

-   -   1. Use the gain factors for BFKD    -   2. Set switch A (952) to 1    -   3. Set switch B to 0    -   4. Trigger switch C so the current non-linear symbol estimate is        used every time except at the beginning of a symbol boundary. In        which case, non-linear symbol estimate from the previous stage        is used.    -   5. Select <TBD> function    -   6. Set the likelihood term ξ_(km)(t)=0

FIG. 10 is a schematic diagram of an embodiment of a user amplitudeestimator 1000 which can be used in the previously described MUDPEs 900a, 900 b. As previously described, a second accumulation of the matchedfilter output is performed that is always initialized to zero at thestart of a symbol, and not normalized. This is referred to as thematched filter accumulator input, which is received by the useramplitude estimator 1000. Additional inputs include the fractional partof the timing offset, the complex channel estimate, and 2 times thereciprocal of the innovation variance (2/λ²) as shown. Regarding thefractional part of the timing offset, in the case of timing offset to1/16th chip resolution, this number will be a 4-bit quantity 0-15 withall bits to the right of the binary point. This value will be used tolookup the pilot power for that phase. The pilot power look up table(LUT) 1010 pre-stores the pilot power corresponding to the phase toprovide this information. The value of 2 times the reciprocal of theinnovation variance is the value previously described as being suppliedto the rest of the MUDPE. The complex channel estimate is obtained fromthe previously described CTCE module.

The user's relative amplitude is a positive number typically less thanone which measures the ratio of the user's amplitude to that of thepilot. The user amplitude estimator 1000 will compute a point-estimateof the square of this quantity every symbol, and will then take a convexcombination of the point estimate and the prior current estimate of thesquare of the user's relative amplitude. More specifically, for theparameter α, which in the figure is 0.99, the estimator 1000 takes 0.01(1−α) times the point estimate plus 0.99 (α) times the prior estimate.The result is clocked at the symbol rate (1014) and the square root ofthe result (1016) is multiplied by the complex channel estimate toprovide the user's complex amplitude estimate.

The point estimate is computed by taking the magnitude squared (1002) ofthe matched filter accumulator output at the end of a symbol andmultiplying it by the reciprocal of the prior estimate (1004) of theuser's relative amplitude squared. The result is than multiplied by ascale factor and a bias is removed. Finally the point estimate islimited to the range 0 to 1 (1008). The square root of the new estimateof the user's relative amplitude squared is taken and then multiplied bythe complex channel amplitude estimate to obtain the complex amplitudeestimate for that user. The scale and bias terms used in the calculationare computed as follows. The magnitude squared of the complex channelestimate (1012) is multiplied by twice the reciprocal of the innovationvariance. It is also multiplied by the pilot power, as provided from thepilot power LUT 1010, which may be different depending on the 4-bitsdenoting the fractional part of the timing offset. The reciprocal istaken of the result (1018) as the bias. The quantity is then multipliedby twice the reciprocal of the innovation variance and the outputsquared (1006) to produce the scale.

FIG. 11 is a schematic diagram illustrating an embodiment of a signaturesynthesizer 1100, which can be used by the previously introduced pilotacquisition, CTCE, and pilot cancellation modules and the MUDPE. Thereal and imaginary pilots are computed using linear feedback shiftregisters (LFSR) 1104, 1106 as would be specified in a standard such asIS-95. For each of the 64 Walsh channels, a different code is appliedfrom the Walsh table 1102. The result is a binary sequence. A “0” bit ismapped to a symbol of 1, and a “1” bit is mapped to the symbol −1. Toproduce an interpolated version of the pilot at one of the 16 requiredfractional offset, the binary input must be filtered 1108, 1110. In thepreferred embodiment, this filter is a 12-tap finite impulse response(FIR) filter. The result is either the pilot synthesized in the case ofWalsh code 0 (which is all 1's) or the signature sequence for any otherWalsh channel.

FIG. 12 is a schematic diagram illustrating sub-chip interpolationfilters that can be used by the MUDPE signature synthesizer 1200, moreparticularly three different implementations 1202, 1204, 1206. Since theinput is binary, the output can be calculated using a look up table.There are several tradeoffs to be made in the implementation dependingon the cost of the lookup table vs. the cost of using adders.Preferably, since there are 16 possible fractional offsets, 4 bits mustalso be used to select the correct filter. The one table implementation1202 requires 16-bits (12-bits for the data input plus 4 bits to selectwhich fractional offset) or 65536 locations to produce the output, butuses no additional logic. The two-table implementation 1204 requires two10-bit tables, or 2 times 1024=2048 locations. For the 10 bits, 4 bitsselect the fractional offset, and the other 6 bits are either the firsthalf of the 12-bit sequence or the second half. The outputs of the twotables must be added together to realize the 12-tap FIR filter. In thethree-table implementation 1206, three 8-bit tables are required, or3×256=768 locations. For the 8 bits, 4 bits select the fractional offsetand the other 4 bits are either the 1st, 2nd, or 3rd 4-bit segment ofthe 12-bit input sequence.

Thus embodiments of the present invention produce and provide improvedinterference cancellation in a CDMA communications environment. Althoughthe present invention has been described in considerable detail withreference to certain embodiments thereof, the invention may be variouslyembodied without departing from the spirit or scope of the invention.Therefore, the following claims should not be limited to the descriptionof the embodiments contained herein in any way.

1. A method for canceling multiple user interference in a communicationssystem wherein a plurality of users communicate over a shared channel,the method comprising: receiving an input that provides a plurality ofdiscrete values produced at sub-symbol intervals that are less than afull symbol period, wherein a current discrete value corresponds to acurrent sub-symbol interval for a current symbol and a previous discretevalue corresponds to a previous sub-symbol interval for the currentsymbol; and estimating symbols for a given user from the plurality ofusers at sub-symbol intervals, wherein a current estimation for thegiven user estimates a portion of the current discrete value thatcorresponds to the current symbol for the given user and cancelsinterference produced by the plurality of users as determined from theprevious discrete value during the previous sub-symbol interval.
 2. Themethod of claim 1, wherein the communications system is a code divisionmultiplex access communications system.
 3. The method of claim 1,wherein the sub-symbol intervals are chip intervals.
 4. The method ofclaim 1, wherein estimating symbols for the given user is performed by aplurality of processing stages.
 5. The method of claim 1, wherein aplurality of processing elements respectively perform estimations foreach of the plurality of users at sub-symbol intervals to accommodatecanceling interference produced by the plurality of users.
 6. The methodof claim 4, wherein the plurality of processing stages includes a firststage and a second stage, the first stage providing an accumulated softsymbol output for the given user to the second stage, the second stageestimating symbols for the given user using the accumulated soft symboloutput.
 7. The method of claim 6, wherein the input as received by thesecond stage is delayed by a symbol period relative to that received bythe first stage.
 8. The method of claim 1, wherein symbol estimation isbased upon one of a minimum mean squared error estimate.
 9. The methodof claim 8, wherein the minimum mean squared error estimate is a linearmean squared error estimate.
 10. The method of claim 1, wherein symbolestimation is based upon a mixed Gaussian distribution.
 11. The methodof claim 4, wherein individual stages from the plurality of processingstages are of different types.
 12. The method of claim 4, wherein eachprocessing stage in the plurality of processing stages performs a multiuser detection algorithm selected from the group consisting of mixedGaussian (MG), decoupled Kalman (DK), parallel interference cancellation(PIC), and partial parallel interference cancellation (PPIC).
 13. Themethod of claim 4, wherein each processing stage in the plurality ofprocessing stages includes a recursive multistage demodulator.
 14. Themethod of claim 13, wherein the recursive multistage demodulatorincludes gain factor and non-linear function modules that arereconfigurable to allow corresponding processing stages to perform amulti user detection algorithm selected from the group consisting ofmixed Gaussian (MG), decoupled Kalman (DK), parallel interferencecancellation (PIC), and partial parallel interference cancellation(PPIC).
 15. An apparatus for canceling multiple user interference in acommunications system wherein a plurality of users communicate over ashared channel, the method comprising: an input that receives aplurality of discrete values produced at sub-symbol intervals that areless than a full symbol period, wherein a current discrete valuecorresponds to a current sub-symbol interval for a current symbol and aprevious discrete value corresponds to a previous sub-symbol intervalfor the current symbol; and a first processing stage, in communicationwith the input, that estimates symbols for a given user at sub-symbolintervals, wherein a current estimation for the given user estimates aportion of the current discrete value that corresponds to the currentsymbol for the given user and cancels interference produced by theplurality of users as determined from the previous discrete value duringthe previous sub-symbol interval.
 16. The apparatus of claim 15, whereinthe communications system is a code division multiplex accesscommunications system.
 17. The apparatus of claim 15, wherein thesub-symbol intervals are chip intervals.
 18. The apparatus of claim 15,wherein the first processing stage is one of a plurality of processingstages used to estimate symbols for the given user.
 19. The apparatus ofclaim 15, wherein the processing stage comprises a plurality ofprocessing elements that respectively perform estimations for each ofthe plurality of users at sub-symbol intervals to accommodate cancelinginterference produced by the plurality of users.
 20. The apparatus ofclaim 18, wherein the plurality of processing stages includes a firststage and a second stage, the first stage providing an accumulated softsymbol output for the given user to the second stage, the second stageestimating symbols for the given user using the accumulated soft symboloutput.
 21. The apparatus of claim 15, wherein the first processingstage implements a minimum mean squared error estimate in estimatingsymbols.
 22. The apparatus of claim 21, wherein the minimum mean squarederror estimate is a linear mean squared error estimate.
 23. Theapparatus of claim 15, wherein the first processing stage implements amixed Gaussian distribution in estimating symbols.
 24. The apparatus ofclaim 18, wherein individual stages from the plurality of processingstages are of different types.
 25. The apparatus of claim 18, whereineach processing stage in the plurality of processing stages performs amulti user detection algorithm selected from the group consisting ofmixed Gaussian (MG), decoupled Kalman (DK), parallel interferencecancellation (PIC), and partial parallel interference cancellation(PPIC).
 26. The apparatus of claim 18, wherein each processing stage inthe plurality of processing stages includes a recursive multistagedemodulator, the recursive multistage demodulator further including gainfactor and non-linear function modules that are reconfigurable toprovide corresponding processing stages that perform a multi userdetection algorithm selected from the group consisting of mixed Gaussian(MG), decoupled Kalman (DK), parallel interference cancellation (PIC),and partial parallel interference cancellation (PPIC).
 27. A computerprogram product for canceling multiple user interference in acommunications system wherein a plurality of users communicate over ashared channel, the computer program product stored on a computerreadable medium and adapted to perform operations comprising: receivingan input that provides a plurality of discrete values produced atsub-symbol intervals that are less than a full symbol period, wherein acurrent discrete value corresponds to a current sub-symbol interval fora current symbol and a previous discrete value corresponds to a previoussub-symbol interval for the current symbol; and estimating symbols for agiven user from the plurality of users at sub-symbol intervals, whereina current estimation for the given user estimates a portion of thecurrent discrete value that corresponds to the current symbol for thegiven user and cancels interference produced by the plurality of usersas determined from the previous discrete value during the previoussub-symbol interval.
 28. The computer program product of claim 27,wherein the communications system is a code division multiplex accesscommunications system.
 29. The computer program product of claim 27,wherein the sub-symbol intervals are chip intervals.
 30. The computerprogram product of claim 27, wherein the symbols are multiple bitsymbols.
 31. A method for canceling multiple user interference in acommunications system wherein a plurality of users communicate over ashared channel, the method comprising: receiving an input that providesa plurality of discrete values produced at sub-symbol intervals that areless than a full symbol period, wherein a current discrete valuecorresponds to a current sub-symbol interval for a current multiple bitsymbol and a previous discrete value corresponds to a previoussub-symbol interval for the current multiple bit symbol; and estimatingsymbols for a given user from the plurality of users at sub-symbolintervals, wherein a current estimation for the given user estimates aportion of the current discrete value that corresponds to the currentmultiple bit symbol for the given user and cancels interference producedby the plurality of users as determined from the previous discrete valueduring the previous sub-symbol interval.
 32. The method of claim 31,wherein the communications system is a code division multiplex accesscommunications system.
 33. The method of claim 31, wherein thesub-symbol intervals are chip intervals.
 34. The method of claim 31,wherein estimating symbols for the given user is performed by aplurality of processing stages.
 35. The method of claim 31, wherein aplurality of processing elements respectively perform estimations foreach of the plurality of users at sub-symbol intervals to accommodatecanceling interference produced by the plurality of users.
 36. Themethod of claim 34, wherein the plurality of processing stages includesa first stage and a second stage, the first stage providing anaccumulated soft symbol output for the given user to the second stage,the second stage estimating symbols for the given user using theaccumulated soft symbol output.
 37. The method of claim 36, wherein theinput as received by the second stage is delayed by a symbol periodrelative to that received by the first stage.
 38. The method of claim31, wherein symbol estimation is based upon one of a minimum meansquared error estimate.
 39. The method of claim 38, wherein the minimummean squared error estimate is a linear mean squared error estimate. 40.The method of claim 31, wherein symbol estimation is based upon a mixedGaussian distribution.
 41. The method of claim 34, wherein individualstages from the plurality of processing stages are of different types.42. The method of claim 34, wherein each processing stage in theplurality of processing stages performs a multi user detection algorithmselected from the group consisting of mixed Gaussian (MG), decoupledKalman (DK), parallel interference cancellation (PIC), and partialparallel interference cancellation (PPIC).
 43. The method of claim 34,wherein each processing stage in the plurality of processing stagesincludes a recursive multistage demodulator.
 44. The method of claim 43,wherein the recursive multistage demodulator includes gain factor andnon-linear function modules that are reconfigurable to allowcorresponding processing stages to perform a multi user detectionalgorithm selected from the group consisting of mixed Gaussian (MG),decoupled Kalman (DK), parallel interference cancellation (PIC), andpartial parallel interference cancellation (PPIC).
 45. An apparatus forcanceling multiple user interference in a communications system whereina plurality of users communicate over a shared channel, the methodcomprising: an input that receives a plurality of discrete valuesproduced at sub-symbol intervals that are less than a full symbolperiod, wherein a current discrete value corresponds to a currentsub-symbol interval for a current multiple bit symbol and a previousdiscrete value corresponds to a previous sub-symbol interval for thecurrent multiple bit symbol; and a first processing stage, incommunication with the input, that estimates symbols for a given user atsub-symbol intervals, wherein a current estimation for the given userestimates a portion of the current discrete value that corresponds tothe current multiple bit symbol for the given user and cancelsinterference produced by the plurality of users as determined from theprevious discrete value during the previous sub-symbol interval.
 46. Theapparatus of claim 45, wherein the communications system is a codedivision multiplex access communications system.
 47. The apparatus ofclaim 45, wherein the sub-symbol intervals are chip intervals.
 48. Theapparatus of claim 45, wherein the first processing stage is one of aplurality of processing stages used to estimate symbols for the givenuser.
 49. The apparatus of claim 45, wherein the processing stagecomprises a plurality of processing elements that respectively performestimations for each of the plurality of users at sub-symbol intervalsto accommodate canceling interference produced by the plurality ofusers.
 50. The apparatus of claim 48, wherein the plurality ofprocessing stages includes a first stage and a second stage, the firststage providing an accumulated soft symbol output for the given user tothe second stage, the second stage estimating symbols for the given userusing the accumulated soft symbol output.
 51. The apparatus of claim 45,wherein the first processing stage implements a minimum mean squarederror estimate in estimating symbols.
 52. The apparatus of claim 51,wherein the minimum mean squared error estimate is a linear mean squarederror estimate.
 53. The apparatus of claim 45, wherein the firstprocessing stage implements a mixed Gaussian distribution in estimatingsymbols.
 54. The apparatus of claim 48, wherein individual stages fromthe plurality of processing stages are of different types.
 55. Theapparatus of claim 48, wherein each processing stage in the plurality ofprocessing stages performs a multi user detection algorithm selectedfrom the group consisting of mixed Gaussian (MG), decoupled Kalman (DK),parallel interference cancellation (PIC), and partial parallelinterference cancellation (PPIC).
 56. The apparatus of claim 48, whereineach processing stage in the plurality of processing stages includes arecursive multistage demodulator, the recursive multistage demodulatorfurther including gain factor and non-linear function modules that arereconfigurable to provide corresponding processing stages that perform amulti user detection algorithm selected from the group consisting ofmixed Gaussian (MG), decoupled Kalman (DK), parallel interferencecancellation (PIC), and partial parallel interference cancellation(PPIC).
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